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.0em plus.0ex minus.2ex-.5emTransfer Detection Benchmark. We explore the potential of F-VLM as a general-purpose detector for different data sources with a view to move towards non dataset-specific detection. F-VLM trained on one dataset can be directly applied to another by swapping out the vocabulary without any finetuning, e.g., replacing the 1203 LVIS categories with COCO 80 categories. The models we use are trained on LVIS base categories and tested on COCO and Objects365-v1 validation splits following the transfer setup of ViLD [1]}. These two datasets have smaller vocabularies than LVIS, and category and image overlaps are hard to avoid, e.g., COCO vs LVIS.
